GATRes

Graph Attention NeTwork with Residual connections

This is the official repository for the paper: "Graph Neural Networks for Pressure Estimation in Water Distribution Systems".

UPDATE: (25/03/2025)

We refactorize the structure to use this library as a separable python package.

By default, all generation flags are set to False. This allows user to choose which parameter is samplied.

First, you can create a dataset based on the default config using the below command:

python gnn_pressure_estimation/scenegenv7.py --executors 2 --batch_size 50 --gen_demand --gen_res_total_head --config "configs/v7.1/ctown_7v1__EPYNET_config.ini

The command samples nodal demand and reservoir total head while keeping other values as they are in the INP file. Note that, in very rare cases, dataset creation may fail due to stochastic nature. If this happens, try generating it again.

The created dataset, ctown.zip, should be located in the datasets folder.

Next, we can train GATRes using the command:

python gnn_pressure_estimation/train.py --model gatres_small --dataset_paths "datasets/ctown.zip" --input_paths "inputs/ctown.inp" --batch_size 32

Installation

You can follow the instructions to run the data generation tool and GATRes models.

1. Clone or download this repository.

2. Activate venv environment with available Python (>=3.9), PyTorch (>=2.0), PyTorch Geometric (>=2.3), and/or CUDA (11.8). In case of a new environment, consider installing the required libraries via this command:

   pip install -r requirement.txt

Repo map

|-- checkpoints - dir for storing model checkpoints #created in dataset generation process |-- configs - config storage for data generation tool |-- experiment_logs - dir for tracking experiment logs #created for loading model weights |-- generator - code for data generation tool |-- inputs - water distribution network topologies |-- utils - auxiliary functions for running models |-- ConfigModels.py - default model configurations for running models |-- GraphModels.py - model definition |-- evaluation.py - code for testing model |-- train.py - code for training model

Data generation tool

This section will give you an instruction to generate an example dataset. A dataset is a set of arbitrary snapshots, an instantaneous water network state at a particular time. A state can consist of one (some) measurement, such as pressures, heads, demands, etc... To create snapshots, run the following steps:

1. Add a preferred input file (.INP) that contains a topology into the inputs folder. Note: Skip this step if you use default public water networks in the folder (under public licenses).

2. Add a data generation config (.INI) file. User can create a dummy one using this command: python generator\EPYNET\ConfigCreator.py -r inputs\<your_inp_path> You can find the created config file (.INI) in the configs folder.

3. Run this command to create a dataset: python generator\EPYNET\scenegenv7.py --config <your_ini_path> --executors <num_of_workers> --batch_size <batch_size_per_worker> There are many flags and options introduced in scenegenv7.py.

The new dataset will be a zip file (.ZIP).

Training model

User can train GATRes model using this default command: python train.py --model gatres_small --epochs 500 --batch_size 8 --device 'cuda' --mask_rate 0.95 --datasets_paths <dataset_path1> <optional_dataset_path2> ... --input_paths <inp_path1> <optional_inp_path2> ... --test_data_path <test_dataset_path> --test_input_path <test_dataset_path> --save_path <where_to_save_checkpoints>

Inference

When you run the above command, the trained model will be tested immediately after training. Otherwise, you can make a manual test using this command:

python evaluation.py --model gatres_small --model_path <where_to_load_trained_weights> --batch_size 8 --num_test_trials 10 --test_type 'clean' --device 'cuda' --mask_rate 0.95 --datasets_paths <train_dataset_path> --input_paths <train_inp_path> --test_data_path <test_dataset_path> --test_input_path <test_dataset_path>

License

MIT license. See the LICENSE file for more details.

Citing

Please cite the following paper if you use the GATRes model or the Data Generation Tool in your research:

``` @article{truong2024gatres, author = {Truong, Huy and Tello, Andrés and Lazovik, Alexander and Degeler, Victoria}, title = {Graph Neural Networks for Pressure Estimation in Water Distribution Systems}, journal = {Water Resources Research}, volume = {60}, number = {7}, pages = {e2023WR036741}, keywords = {graph neural networks, water distribution networks, pressure estimation, state estimation, water distribution systems, water management}, doi = {https://doi.org/10.1029/2023WR036741}, url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2023WR036741}, eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2023WR036741}, note = {e2023WR036741 2023WR036741}, year = {2024} }

```